Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs. - Vinitra Swamy; Bahar Radmehr; Natasa Krco; Mirko Marras; Tanja Käser (EDM, 2022)

When Mitigating Bias is Unfair: A Comprehensive Study on the Impact of Bias Mitigation Algorithms. - Natasa Krco; Thibault Laugel; Jean-Michel Loubes; Marcin Detyniecki (CoRR, 2023)

Lost in the Averages: A New Specific Setup to Evaluate Membership Inference Attacks Against Machine Learning Models. - Florent Guépin; Natasa Krco; Matthieu Meeus; Yves-Alexandre de Montjoye (CoRR, 2024)

When Mitigating Bias is Unfair: Multiplicity and Arbitrariness in Algorithmic Group Fairness. - Natasa Krco; Thibault Laugel; Vincent Grari; Jean-Michel Loubes; Marcin Detyniecki (SaTML, 2025)

The DCR Delusion: Measuring the Privacy Risk of Synthetic Data. - Zexi Yao; Natasa Krco; Georgi Ganev; Yves-Alexandre de Montjoye (CoRR, 2025)